Depth-aware blur kernel estimation method for iris deblurring

ABSTRACT

Estimating a blur kernel distribution for visual iris recognition includes determining a first mathematical relationship between an in-focus position of a camera lens and a distance between the lens and an iris whose image is to be captured by the lens. A second mathematical relationship between the in-focus position of the lens and a standard deviation defining a Gaussian blur kernel distribution is estimated. The first mathematical relationship is used to ascertain a desired focus position of the lens based upon the actual position of the living being&#39;s eye at the point in time. The second mathematical relationship is used to calculate a standard deviation defining a Gaussian blur kernel distribution. The produced image is digitally unblurred by using the blur kernel distribution defined by the calculated standard deviation.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This application is a continuation of U.S. patent application Ser. No.12/367,069 entitled “A DEPTH-AWARE BLUR KERNEL ESTIMATION METHOD FORIRIS DEBLURRING” (Attorney Docket No. 14347-420-US-1), filed Feb. 6,2009. The complete subject matter of this patent application is herebyincorporated herein by reference, in its entirety.

BACKGROUND

1. Field of the Invention

The present invention relates to apparatuses and methods for identifyingpersonnel and, more particularly, to apparatuses and methods foridentifying personnel based on visual characteristics of the irises oftheir eyes.

2. Description of the Related Art

Iris recognition, or “iris capture” is a method of biometric personalidentification that uses pattern recognition algorithms based on imagesof at least one of the irises of an individual's eyes. Iris recognitionuses camera technology to produce images of the details of the iris.These images are converted into digital templates and providemathematical representations of the iris that are used to identifyindividuals.

Due to hardware limitations, the images that are captured are oftenblurred. However, iris image deblurring can greatly improve therobustness for less intrusive iris capture systems. Most known irisdeblurring algorithms ignore the characteristics of the capture systemitself, and focus only on features that are directly computed from theiris images. More particularly, in many iris capture systems, systemdelay is often seen between the identified capturing event and the realshutter release of the camera. The delay may consist of several parts,the time to drive the lens to the desired position, the time to releasethe shutter and capture the image, and the time to drive the pan-tiltunit. Therefore, if a user is moving, the focused depth of the cameraand the actual depth of the eyes are usually different. When the depthdifference is larger than the depth of the field (DOF) of the camera,the defocus blur occurs, which can greatly deteriorate the performanceof the iris recognition.

What is neither disclosed nor suggested in the art is a method of irisimage deblurring that is able to compensate for focusing characteristicsof the camera in cases in which it is not practical to achieve an idealfocus position of the lens before the image is captured.

SUMMARY

The present invention provides a novel method that uses depthinformation (i.e., the distance from the camera to the user) to estimatethe blur kernel parameters for iris deblurring. The method may apply toiris capture systems in which accurate depth information can beacquired. The method of the invention may handle defocus blur when thereal focus position cannot be estimated by the depth information becauseof the typical system delay often seen in iris capture systems. Theinvention may enable defocus blur to be corrected when the focusposition of the camera cannot be adjusted quickly enough to keep up withthe movement of the subject.

If the accurate depth information can be acquired in real time, thesystem can predict the desired focus position based on the speed of theuser. Even in this scenario, however, it is possible for the predictedposition of the user, and hence the predicted focus position, to stillbe inaccurate. In some embodiments, the present invention may correctthe defocus blur in cases in which the position of the user cannot bepredicted with sufficient accuracy to capture the image with anacceptable focus position.

In one embodiment, the present invention comprises a method ofestimating a blur kernel distribution for visual iris recognitionincludes determining a first mathematical relationship between anin-focus position of a camera lens and a distance between the camera andan iris whose image is to be captured by the camera. A secondmathematical relationship between the in-focus position of the lens anda standard deviation defining a Gaussian blur kernel distribution isestimated. A position of an eye of a living being at a future point intime is predicted. A focus position of the camera lens is adjusted basedupon the predicted position of the eye. The camera lens with theadjusted focus position is used to produce an image of the livingbeing's eye at the point in time. An actual position of the livingbeing's eye at the point in time is sensed. The first mathematicalrelationship is used to ascertain a desired focus position of the lensbased upon the actual position of the living being's eye at the point intime. The second mathematical relationship is used to calculate astandard deviation defining a Gaussian blur kernel distribution. Thecalculating is based upon a difference between the adjusted focusposition and the desired focus position of the lens. The produced imageis digitally unblurred by using the blur kernel distribution defined bythe calculated standard deviation.

In another embodiment, the present invention comprises a method ofvisually recognizing an iris. A position of an eye of a living being ata future point in time is predicted. A focus position of a camera lensis adjusted dependent on the predicted position. The camera lens withthe adjusted focus position is used to produce an image of the livingbeing's eye at the point in time. An actual position of the livingbeing's eye at the point in time is sensed. A Gaussian blur kerneldistribution is determined based upon the adjusted focus position of thecamera lens and the actual position of the living being's eye at thepoint in time. The blur kernel distribution is used to digitally unblurthe produced image.

In yet another embodiment, the present invention comprises a method ofcapturing an image of an iris, including using a camera lens with afocus position to produce an image of a living being's eye. An actualposition of the living being's eye is sensed at a point in time at whichthe camera produced the image of the living being's eye. The producedimage is digitally unblurring based upon the focus position of thecamera lens, and the actual position of the living being's eye at thepoint in time.

An advantage of the present invention is that it makes it possible toaccurately deblur an iris image that has been captured with a camerafocus position that is less than ideal for the distance between thecamera and the iris at the moment in time at which the image iscaptured.

BRIEF DESCRIPTION OF THE DRAWINGS

The above mentioned and other features and objects of this invention,and the manner of attaining them, will become more apparent and theinvention itself will be better understood by reference to the followingdescription of an embodiment of the invention taken in conjunction withthe accompanying drawings, wherein:

FIG. 1 is a block diagram of one embodiment of an iris capture systemaccording to one embodiment of the invention;

FIG. 2 is an operational block diagram of the iris capture system ofFIG. 1;

FIG. 3 is an example of a fitted curve for the measured focus positionsof the camera of the system of FIG. 1 as a function of the depth betweenthe camera lens and the object.

FIG. 4 a illustrates examples of plots of the standard deviation of theblur kernel Gaussian distribution as a function of the focus position ofthe camera of the system of FIG. 1 for various distances between thecamera and the iris according to one embodiment of a method of thepresent invention for visually recognizing an iris.

FIG. 4 b is the plot of FIG. 4 a corresponding to a distance of 3.30meters between the camera and the iris.

FIG. 4 c is the plot of FIG. 4 a corresponding to a distance of 2.97meters between the camera and the iris.

FIG. 4 d is the plot of FIG. 4 a corresponding to a distance of 2.56meters between the camera and the iris.

FIG. 4 e is the plot of FIG. 4 a corresponding to a distance of 2.00meters between the camera and the iris.

FIG. 4 f is the plot of FIG. 4 a corresponding to a distance of 1.58meters between the camera and the iris.

FIG. 4 g is the plot of FIG. 4 a corresponding to a distance of 1.43meters between the camera and the iris.

FIG. 4 h is a plot illustrating how a standard deviation defining a blurkernel distribution appropriate for deblurring may be calculatedaccording to one embodiment of a method of the present invention.

FIG. 5 is a plot of the distributions of image gradients of randomnatural images and of global iris images.

Corresponding reference characters indicate corresponding partsthroughout the several views. Although the drawings representembodiments of the present invention, the drawings are not necessarilyto scale and certain features may be exaggerated in order to betterillustrate and explain the present invention. Although theexemplification set out herein illustrates embodiments of the invention,in several forms, the embodiments disclosed below are not intended to beexhaustive or to be construed as limiting the scope of the invention tothe precise forms disclosed.

DETAILED DESCRIPTION

The embodiments hereinafter disclosed are not intended to be exhaustiveor limit the invention to the precise forms disclosed in the followingdescription. Rather the embodiments are chosen and described so thatothers skilled in the art may utilize its teachings.

Turning now to the drawings, and particularly to FIG. 1, there is shownone embodiment of an iris capture system 20 of the present inventionincluding an NFOV NIR camera 22 with adjustable focus, an NIRilluminator 24, and a depth sensor 26 all in electronic communicationwith a central processor 28. System 20 may capture images of, and detectthe positions of, moving subjects such as a human being 30 or a humanbeing 32 when he approaches a doorway at which camera 22, illuminator 24and sensor 26 are mounted, such as in a direction indicated by arrow 36.Camera 22 may be installed with a mounting height H and tilt angle asuch that a standoff distance 38 for the user is approximately between1.5 meters and 3.5 meters and the captured iris diameter is above 150pixels. In one embodiment, height H is about 250 centimeters. The widthof a capture volume 40 may be on the order of 20 centimeters. In theembodiment illustrated in FIG. 1, a width 42 of capture volume 40 wherethe image and shape of the taller person 30 are captured is about 17centimeters, and a width 44 of capture volume 40 where the image andshape of the shorter person 32 are captured is about 30 centimeters.There are many devices known for measuring depth information, such asstereo cameras, time-of-flight sensors, and structure lights.

In embodiments in which NFOV camera 22 does not have panning and tiltingcapabilities, the human being whose image and shape are being capturedneeds to look at camera 22 while approaching the doorway. The iriscapture may be triggered at different standoff distances for users withdifferent heights.

Depth sensor 26 may be installed at various positions and orientations.Depth sensor 26 may be positioned very close to NFOV camera 22 to allowfor a more compact design. NIR illuminator 24 can be placed at anylocation so long as it illuminates capture volume 40.

System 20 can be applied to other possible settings in which depthsensor 26 is used. For example, camera 22 may be in the form of a highspeed, high performance video camera. Alternatively, camera 22 may havea fixed focus or adjustable focus based on the distance between thecamera and the user. It is also possible for camera 22 to includepan-tilt capabilities in order to further enlarge the capture volume.

An operational block diagram of system 20 is illustrated in FIG. 2. Thethree-dimensional information measured by depth sensor 26 may be used invarious ways within system 20. First, face detection and tracking 46 maybe performed on the up-sampled intensity images 48 captured by depthsensor 26. The three-dimensional position of the eyes may then beestimated from an upper portion of the detected face depth maps. Thenext eye location for the moving subject may be predicted accurately inreal time. For example, time rates of change of the three-dimensionalposition of the eyes may be extrapolated to predict future eyelocations. Second, the three-dimensional position may be used todetermine whether eyes are within the field of view and whether thestand-off distance is within the depth of field. If these two conditionsare satisfied, the NFOV camera may be instructed to perform imagecapturing, as at 50. Third, the depth information may be used todynamically control the focus position of the lens of NFOV camera 22.Finally, the depth information can be used to estimate the blur kernel52 for iris deblurring, as at 53. The deblurring may be useful in aniris recognition algorithm 55. More accurate depth information could beused to predict the speed and future positions of the human being sothat the real or desired focus position can be estimated more accuratelyeven when the system delay exists. The real or desired focus positionmay represent the focus position that is ideal for the future estimatedposition of the human being.

Calibration between NFOV camera 22 and depth sensor 26 may be performed,as at 54. In one embodiment, depth sensor 26 could be a TOF sensor. Manyexisting TOF sensors contain systematic depth bias from the demodulationof correlation function and incident lights, and so calibration, orso-called “precalibration,” of the TOF sensor may obtain a better depthmeasurement. In a first step of a novel calibration method of thepresent invention, a large planar board may be positioned at differentdepths and with different orientations. A robust plane fitting may thenbe applied for the planar board at each position. The depth bias may beestimated by computing the difference between measured depth and thefitted plane. After the calibration of TOF sensor 26, the depthuncertainty may be greatly reduced, especially the depth uncertaintybetween 1.3 and 2 meters. In order to transform the depth in thecoordinate system of TOF sensor 26 to that of NFOV camera 22, a fullsystem calibration may be performed. The NFOV camera with a telephotolens may be approximated as an affine camera. A planar checkerboardpattern is captured at different depths. As the correspondences betweenthe two-dimensional points x from NFOV camera 22 and three-dimensionalpoints X from TOF sensor 26 are known, the projection matrix P can becomputed by minimizing the re-projection errors. The intrinsic andextrinsic matrices may be obtained by RQ decomposition of P.

Blur kernel estimation step 52 for iris deblurring is optional. As longas the iris deblurring algorithm needs to use the accurate depthinformation, the depth information provided by TOF sensor 26 may besufficient. When depth information is not available in capturingsystems, some statistics of the captured image (e.g., focus scores) maybe used to estimate blur kernel.

Image blur may be modeled as a convolution process:

I=L

h+n   (1)

where I, L, h, and n represent the blurred image; un-blurred image;point spread function (PSF) or blur kernel; and additive noise,respectively. For defocus blur, the PSF h depends on the circle ofconfusion R. For cameras with adjustable focus, R is a function of twoparameters based on the typical pin-hole camera model. The twoparameters are the distance from the object to the lens d and thedistance between the lens and the image plane s,

$\begin{matrix}{R = {\frac{Ds}{2}{{\frac{1}{f} - \frac{1}{d} - \frac{1}{s}}}}} & (2)\end{matrix}$

where D is the radius of the lens, and f is the focal length of thelens. For cameras with fixed focus s, R is determined only by d.

The PSF h for the defocus blur may be modeled as a Gaussian kernel,

$\begin{matrix}{h = {\frac{1}{2\pi \; \sigma_{h}^{2}}{^{- \frac{x^{2} + y^{2}}{2\sigma_{h}^{2}}}.}}} & (3)\end{matrix}$

Because the captured eye region is usually parallel to the image plane,the PSF h may be shift-invariant.

The blur kernel estimation method of the present invention will now bedescribed with the assumption in place that the depth difference ismeasured. When the fixed focus cameras are used, it is relatively simpleto estimate the kernel. The kernel estimation method of the presentinvention may deal with the more general case, i.e., cameras withadjustable focus. As mentioned above, the depth difference may be mainlycaused by the system delay when a subject is moving.

As the lens focus position p_(f) is proportional to the distance betweenthe lens and image plane s, when the circle of confusion R is smallenough, the relationship between the in-focus position of lens p_(f) andd may be derived based on Equation (2),

$\begin{matrix}{p_{f} = {\frac{d}{{k_{1}d} + k_{2\;}}.}} & (4)\end{matrix}$

After measuring focus positions from in-focus images at differentdepths, k₁ and k₂ can be easily estimated by curve fitting usingEquation (4). FIG. 3 shows an example of a fitted curve for the measuredfocus positions and depths.

As the standard deviation of the blur kernel Gaussian distribution σ_(h)is proportional to R and s is proportional to p_(f), when d is fixed,the relationship between σ_(h) and p_(f) may be derived, based onEquation (2),

σ_(h) =|k ₃ p _(f) +k ₄|.   (5)

Although the parameters k₁, k₂, k₃ and k₄ are characteristics of thecamera system, they have no obvious physical meaning or representation.The standard deviation σ_(h), which defines the blur kernel Gaussiandistribution, cannot be measured directly. Thus, the following novelalgorithm of the present invention may estimate σ_(h) and then learn k₃and k₄ accordingly.

In a first step of the algorithm, in-focus and defocused checkerboardimages are captured under different depths and different focuspositions. As in-focus and defocused images are known, only σ_(h) isunknown. The standard deviation σ_(h) is estimated by ar gmin_(σh)∥I−L

h∥₂ ². The subscript 2 in the formula denotes a Euclidean Norm or aL2-Norm.

In a next step, k₃ and k₄ are estimated by argmin_(k3,k4)∥k₃p_(f)+k₄−σ_(h)∥₂ ². FIGS. 4 a-g show examples of thefitting results for p_(f) and σ_(h) based on Equation (5). FIGS. 4 a-gare plots of the focus position of camera 22 versus a standard deviationof the blur kernel distribution for six different distances betweencamera 22 and the subject iris. The plot for each of the six distancesis V-shaped, with the origin of the “V” being at the in-focus positioncorresponding to that distance. The parameter k₃ may represent the slopeof a corresponding V-shaped plot in FIGS. 4 a-g; and parameter k₄ mayrepresent the y-intercept of the corresponding V-shaped plot. V-shapedplot 60 corresponds to a distance of about 3.30 meters; V-shaped plot 62corresponds to a distance of about 2.97 meters; V-shaped plot 64corresponds to a distance of about 2.56 meters; V-shaped plot 66corresponds to a distance of about 2.00 meters; V-shaped plot 68corresponds to a distance of about 1.58 meters; and V-shaped plot 70corresponds to a distance of about 1.43 meters.

Each of the circles in FIGS. 4 a-g represents an empirically-collecteddata point. The data points at the top (standard deviation=20) of FIGS.4 a-g are the images that are severely blurred. It may not be feasibleto recover these kinds of severely blurred images in practice even witha large kernel size. Hence, these severely blurred images are treated asoutliers and are not included in the estimation.

Based on FIGS. 3 and 4 a-g, it can be concluded that the modelsdescribed in Equations (4) and (5) may be used for real camera systemseven though the derivation of Equations (4) and (5) is based on thetraditional pin-hole camera model. A practical use of the plots of FIGS.4 a-g is to estimate the blur kernel when the subject is moving.

When a user enters the field of view of the capturing system, thethree-dimensional position of the user's eyes after the system delay maybe predicted. When the predicted eye position satisfies the triggeringcondition, the predicted in-focus position p_(f)is computed usingEquation (4) and the image is produced at this position. The correct(i.e., actual) depth at the time of image capture (after the systemdelay) is measured, and the correct or ideal in-focus position p _(f)corresponding to the actual depth measurement is computed. For example,assuming the correct or ideal in-focus position p _(f) is 15 (as shownas the origin of the V-shaped plot in FIG. 4 h) for an actual, measureddepth, a new model can be interpolated (i.e., Equation (5) withdifferent values for k₃ and k₄). The new model is illustrated as thedashed V-shaped plot originating at focus position 15 in FIG. 4 h.Assuming the predicted in-focus position {tilde over (p)}_(f) that wasactually used to produce the iris image is 13.5, as indicated by therectangle at 13.5 in FIG. 4 h, the standard deviation σ_(h) that definesthe blur kernel distribution appropriate for use in deblurring is shownto be approximately 8 in FIG. 4 h. The standard deviation σ_(h) may becomputed by taking the predicted focus position of 13.5 that wasactually used to produce the image, and plugging that value of 13.5 intoEquation (5) along with the values of k₃ and k₄ that correspond to theactual depth measurement (i.e., the actual depth measurement thatcorresponds to an ideal focus position of 15).

The above-described calculation of the blur kernel Gaussian distributionmay be used to unblur a captured blurred image as described in detailbelow. Particularly, the process of image deblurring may be formulatedin the Bayesian framework by Bayes' theorem,

P(L|σ_(h), I)∝P(I|L, σ_(h))P(L)

where P(I|L,σ_(h)) is the likelihood that L is the clear image given ablur kernel defined by a Gaussian distribution that is, in turn, definedby a standard deviation σ_(h). P(L) represents the prior on theun-blurred image L. A prior probability, or a “prior,” is a marginalprobability, interpreted as what is known about a variable in theabsence of some evidence. The posterior probability is then theconditional probability of the variable taking the evidence intoaccount. The posterior probability may be computed from the prior andthe likelihood function via Bayes' theorem.

Different priors chosen in this framework may lead to differentdeblurring algorithms with different performances. The novel irisdeblurring algorithm of the present invention may be applied in any iriscapture system to handle defocus blur. The prior on the un-blurred imageL may depend upon three prior components that are based on global andlocal iris image statistics:

P(L)=P _(g)(L)P _(p)(L)P _(s)(L).

The first prior P_(g)(L) may be computed from an empirically-determinedglobal distribution of the iris image gradients; P_(p)(L) may becomputed based on characteristics of dark pupil region; and P_(s)(L) maybe computed from the pupil saturation region (i.e., the highlight regionof the pupil that is saturated with intensity values of highbrightness). For general image deblurring, the global distribution ofiris image gradients may be approximated by a mixture of Gaussiandistributions, exponential functions, and piece-wise continuousfunctions. Mixture Gaussian distributions are described in “Removingcamera shake from a single photograph”, R. Fergus, B. Singh, A.Hertzmann, S. T. Roweis, and W. T. Freeman, ACM Transactions onGraphics, 2006; exponential functions are described in “Image and depthfrom a conventional camera with a coded aperture”, A. Levin, R. Fergus,F. Durand, and W. T. Freeman, ACM Transactions on Graphics, 2007; andpiece-wise continuous functions are described in “High-quality motiondeblurring from a single image”, Q. Shan, J. Jia, and A. Agarwala, InSIGGRAPH, 2008, each of which is incorporated by reference herein in itsentirety.

Because the application domain is iris images rather than naturalimages, according to one embodiment of the present invention, the globaldistribution may be computed from iris images only. As illustrated inFIG. 5, the distribution of general natural images (i.e., any imagesfound in nature, such as sky, water, landscape) has a greateruncertainty than the distribution of global iris images. The presentinvention takes advantage of the tight range of the global iris imagestatistics.

As a result of the tighter iris image statistics, the distribution ofiris image gradients is a stronger prior. A two-piecewise quadraticfunction (i.e., a piecewise quadratic function having two separate,continuous portions) may be used to approximate the distribution so thatthe optimization based on this Bayesian problem becomes simpler and moreefficient. A general form of the two-piecewise quadratic function maybe:

${P_{g}(L)} \propto \left\{ \begin{matrix}{{\Pi_{i}^{{a_{1}{({\partial L_{i}})}}^{2} + b_{1}}},{{{\partial L_{i\;}}} \leq k}} \\{{\Pi_{i}^{{a_{2}{({\partial L_{i}})}}^{2} + b_{2}}},{{{\partial L_{i}}} > k}}\end{matrix} \right.$

where ∂L_(i) is the gradient for a pixel and k is the threshold betweentwo functions. Such a two-piecewise quadratic function may berepresented by the fitted curve in FIG. 5, wherein the threshold k is atthe transitions between the low frequency and high frequency regions.

The second P_(p)(L) and third P_(s)(L) priors may be computed from thelocal pupil region because the dark pupil region is likely to be smoothas compared with the nearby iris patterns, and the highlight region islikely saturated. Therefore, these two priors may be particularly usefulin recovering nearby iris patterns. As the smooth pupil region tends tohave small gradients that are not sensitive to the defocus blur, and thesaturated highlight region tends to contain the highest intensity, thetwo priors may be computed as following:

${P_{p}(L)} \propto {\prod\limits_{i \in \Omega_{1}}{N\left( {\left. {{\partial L_{i}} - {\partial I_{i}}} \middle| 0 \right.,\sigma_{p}} \right)}}$${{P_{s}(L)} \propto {\prod\limits_{i \in \Omega_{2}}{N\left( {\left. {L_{i} - 255} \middle| 0 \right.,\sigma_{s}} \right)}}},$

where Ω₁ is the dark pupil region (i.e., excluding the highlightregion), and Ω₂ is the saturated highlight region within the pupil. Thedark pupil region and the saturated highlight region within the pupilcan be detected by image processing techniques, such as thresholding,erosion and dilation. The 255 term in the P,(L) formula represents thehighest (i.e., whitest) color value on a scale of 0 to 255.

Putting all of these priors together, this iris deblurring problem maybe solved by minimizing an energy function E in the following quadraticform:

E ∝ I − L ⊗ h² + λ₁(a₁(∂L)² + b₁ ⋅ M₁ + a₂(∂L)² + b₂ ⋅ M₂) + λ₂(∂L − ∂I² ⋅ M₃ + L − 255² ⋅ M₄),

where M₁, M₂, M₃, and M₄ are masks of low-frequency region,high-frequency region, dark pupil region, and highlight region in thepupil; I is the known blurred image captured by the camera lens; h isthe blur kernel, which may be estimated as discussed in detail above;and L is the clear image that is being determined. Thus, given knownvalues for the blurred image I and the blur kernel h, an image L may bedetermined that minimizes E, and this image L may be used as arepresentation of a clear, unblurred version of the produced blurredimage I.

The deblur kernel h can be estimated based on the depth information orfocus scores. If the blur kernel is not known, it is possible to add aGaussian prior in place of the blur kernel in order to convert thenon-blind deconvolution into a blind one, which still can be solved bythe optimization framework.

While this invention has been described as having an exemplary design,the present invention may be further modified within the spirit andscope of this disclosure. This application is therefore intended tocover any variations, uses, or adaptations of the invention using itsgeneral principles. Further, this application is intended to cover suchdepartures from the present disclosure as come within known or customarypractice in the art to which this invention pertains.

What is claimed is:
 1. A method of estimating a blur kernel distributionfor visual iris recognition, said method comprising the steps of:determining a first mathematical relationship between an in-focusposition of a camera lens and a distance between the lens and an iriswhose image is to be captured by the lens; estimating a secondmathematical relationship between the in-focus position of the lens anda standard deviation defining a Gaussian blur kernel distribution;predicting a position of an eye of a living being at a future point intime; adjusting a focus position of the camera lens based upon thepredicted position of the eye; using the camera lens with the adjustedfocus position to produce an image of the living being's eye at thepoint in time; sensing an actual position of the living being's eye atthe point in time; using the first mathematical relationship toascertain a desired focus position of the lens based upon the actualposition of the living being's eye at the point in time; using thesecond mathematical relationship to calculate a standard deviationdefining a Gaussian blur kernel distribution, the calculating beingbased upon a difference between the adjusted focus position and thedesired focus position of the lens; and digitally unblurring theproduced image by using the blur kernel distribution defined by thecalculated standard deviation.
 2. The method of claim 1 wherein thefirst mathematical relationship is determined based on empirical data.3. The method of claim 1 wherein the second mathematical relationship isa linear relationship that is dependent upon the first mathematicalrelationship.
 4. The method of claim 1 wherein a fixed position is usedas a proxy for the predicted position of the eye.
 5. The method of claim1 wherein the first mathematical relationship is used in adjusting thefocus position of the camera.
 6. The method of claim 1 wherein thesensing step is performed using a depth sensor.
 7. The method of claim 1comprising the further step of determining whether the captured imageneeds to be modified based on the estimated blur kernel, which isrealized by identifying a defocus-blur level of the captured iris imagebased on the estimated blur kernel.
 8. A method of visually recognizingan iris, said method comprising the steps of predicting a position of aneye of a living being at a future point in time; adjusting a focusposition of a camera lens, the adjusting being dependent on thepredicted position; using the camera lens with the adjusted focusposition to produce an image of the living being's eye at the point intime; sensing an actual position of the living being's eye at the pointin time; determining a Gaussian blur kernel distribution based upon: theadjusted focus position of the camera lens; and the actual position ofthe living being's eye at the point in time; and using the blur kerneldistribution to digitally unblur the produced image.
 9. The method ofclaim 8 comprising the further steps of: collecting empirical dataregarding in-focus positions of the lens at various distances betweenthe lens and an iris whose image is to be captured by the lens;determining, based on the empirical data, a first mathematicalrelationship between the in-focus position of the lens and a distancebetween the camera and an iris whose image is to be captured by thecamera system; and using the first mathematical relationship inadjusting the focus position of the lens.
 10. The method of claim 8comprising the further step of using the first relationship to estimatea second mathematical relationship between the in-focus position of thelens and a standard deviation defining the Gaussian blur kerneldistribution, the determining of the Gaussian blur kernel distributionincluding using the second relationship to calculate the standarddeviation based upon a difference between: the adjusted focus positionof the camera lens; and a desired focus position of the lenscorresponding to the actual position of the living being's eye at thepoint in time.
 11. The method of claim 10 wherein the secondmathematical relationship is a linear relationship.
 12. The method ofclaim 8 wherein a fixed position is used as a proxy for the predictedposition of the eye.
 13. The method of claim 8 wherein the sensing stepis performed using a depth sensor.
 14. A method of capturing an image ofan iris, said method comprising the steps of: using a camera lens with afocus position to produce an image of a living being's eye; sensing anactual position of the living being's eye at a point in time at whichthe camera lens produced the image of the living being's eye; anddigitally unblurring the produced image based upon: the focus positionof the camera lens; and the actual position of the living being's eye atthe point in time.
 15. The method of claim 14 wherein the digitallyunblurring step comprises: determining a Gaussian blur kerneldistribution based upon: the focus position of the camera lens; and theactual position of the living being's eye at the point in time; andusing the blur kernel distribution to digitally unblur the producedimage.
 16. The method of claim 14 comprising the further step, beforethe camera lens produces the image of the eye, of adjusting the focusposition of the lens based upon a predicted position of the eye at thepoint in time at which the camera lens produces the image of the eye.17. The method of claim 16 comprising the further steps of: collectingempirical data regarding in-focus positions of the lens at variousdistances between the lens and an iris whose image is to be captured bythe lens; determining, based on the empirical data, a first mathematicalrelationship between the in-focus position of the lens and a distancebetween the lens and an iris whose image is to be captured by the lens;and using the first mathematical relationship in adjusting the focusposition of the lens.
 18. The method of claim 17 comprising the furtherstep of using the first relationship to estimate a second mathematicalrelationship between the in-focus position of the lens and a standarddeviation defining a Gaussian blur kernel distribution, the Gaussianblur kernel distribution being used in the digitally deblurring step.19. The method of claim 18 wherein the second mathematical relationshipis a linear relationship.
 20. The method of claim 14 wherein the sensingstep is performed using a depth sensor.